MLOps Engineer 9Core ML + MLOps

EL3 – Databricks MLOps Engineer (Contract)

Role Summary

Key Responsibilities

• Enable and automate the end-to-end ML lifecycle on Databricks (environment setup, model workflow automation, job scheduling, monitoring hooks).
• Build frameworks, templates, and utilities that make ML development and experimentation reproducible and scalable.
• Implement CI/CD pipelines using Git, GitHub Actions, Jenkins, Azure DevOps, or similar tools.
• Package, version, and deploy ML models into Databricks-managed execution environments.
• Set up automated workflows for training, retraining, evaluation, and scheduled job execution.
• Support creation and integration of machine learning models including classification, forecasting, anomaly detection, NLP, and PI models.
• Enable LLM/GenAI-driven solutions by integrating:
• Embedding model generation
• RAG architectures
• Vector databases
• LangChain agentic workflows
• Optimize resource usage, runtime configurations, and code execution patterns for ML workloads.
• Collaborate with Data Scientists to translate experimental notebooks into production-ready pipelines.
• Implement platform-level controls for environment consistency, dependency management, access control, and model versioning.
• Support troubleshooting, debugging, and performance improvements for ML workloads.
• Document standards, templates, guidelines, and best practices for MLOps teams.
• Work cross-functionally with product, engineering, and analytics teams across PI.

Required Qualifications

• Bachelor’s/Master’s degree in Computer Science, Engineering, or related field
• 6–9 years of relevant experience in ML Engineering, MLOps, or platform engineering
• Strong hands-on experience with Databricks, Spark (batch/streaming), Python, Scala
• Experience enabling ML lifecycle tools such as MLflow (tracking, packaging, model registration)
• Strong CI/CD experience using Git, GitHub Actions, Jenkins, or Azure DevOps
• Experience deploying AI/ML models into cloud environments (Azure preferred)
• Ability to create and integrate embedding models, semantic vectors, and LLM-driven components
• Experience with LangChain for agentic workflows and integration of tools/functions
• Strong problem-solving, debugging, and collaboration skills

Preferred Qualifications

• Experience with Azure OpenAI or OpenAI-compatible LLM APIs
• Familiarity with healthcare claims workflows, PI, FWA, provider billing, or pricing
• Experience in Agile/Scrum environments
• Strong understanding of software engineering best practices, packaging, dependency management
• Experience deploying AI/ML models into cloud environments (Azure preferred)
• Good-to-Have Data Knowledge
• Call Center datasets (member & provider interactions)
• Provider RCM datasets (billing, coding, authorizations)
• EHR/clinical datasets for cross-domain validation
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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Good-to-Have Data Knowledge